Former Research Groups

Multisensory Perception and Action (Dr. Marc Ernst)

For perceiving the environment our brain uses multiple sources of sensory information derived from several different modalities, including vision, touch and audition. This multisensory information has to converge in the brain in order to form a coherent percept of the world and in order to generate goal-directed actions. Addressing this global topic the Multisensory Perception and Action Group is divided in three subgroups focusing on multisensory perception, perception for action, and perceptual learning. [more]

Computational Vision and Neuroscience (Prof. Dr. Matthias Bethge)

The research of the Computational Vision and Neuroscience Group aims at elucidating the principles of neural information processing, learning and inference in biological vision. Using methods of statistical inference and learning theory, as well as signal processing, nonlinear dynamics and optimization theory, we address the problem of perceptual inference from natural images and its neural basis at three different levels: natural image statistics, neural population coding/data analysis, and psychophysics. [more]

Cognitive Neuroimaging (Uta Noppeney, PhD)

The goal of the Cognitive Neuroimaging Group is to better understand the neural systems that allow us to acquire, represent and retrieve knowledge about our multi-sensory environment. [more]

Machine Learning and Computational Biology (Dr. Karsten Borgwardt)

Our research area is the development of intelligent algorithms for analysing complex systems in biology. It is located at the intersection of machine learning, data mining and bioinformatics, and contributes to these three fields. Our research reaches deep into statistics, algorithmics and scientific computing. [more]

Physiology of Sensory Integration (Dr. Christoph Kayser)

Our brain is exposed to a flood of sensory inputs. The efficient and reliable processing of these suggests that sensory systems are adapted to the properties of their natural input and employ highly efficient means for encoding and decoding sensory information. Our work aims at understanding the principles underlying sensory information processing and how this is translated into reliable behavioral reactions. [more]

Network Imaging (Dr. Jason Kerr)

Network Imaging Group focuses on understanding the principles underlying spatial & temporal organization of neuronal activity during decision making and object perception in behaving rodents.[more]

Computational Neuroanatomy (Bernstein Group)

One key challenge in neuroscience is to elucidate mechanistic principles of how the brain integrates sensory information from its environment to generate behavior. At present, experimental methods to directly monitor sensory-evoked streams of excitation throughout the brain, at cellular and millisecond resolution are lacking. To overcome these limitations, the ‘Computational Neuroanatomy Group’ seeks to develop an alternative reverse engineering approach. The novel approach comprises reconstructing the detailed 3D structure of neural circuits, quantifying local and long-range synaptic connectivity and simulating sensory-evoked signal flow within the resultant anatomically realistic network models. [more]

Neural Computation and Behaviour (Jakob Macke)

We seek to understand how populations of neurons collectively process sensory input, perform computations and control behaviour. In particular, we are interested in how internal states and processes influence neural activity and perceptual decisions. To this end, we develop statistical models and machine learning algorithms for neural data analysis, and collaborate with experimental laboratories performing measurements of neural activity and behaviour.[more]

Sensorimotor Learning and Decision-Making (Daniel Braun)

One of the most striking features that sets human motor control apart from its robotic counterparts is the remarkable adaptability that allows us to cope with a vast range of complex and variable environments. The research goal of our group is to investigate the computational and biological principles underlying this unrivalled adaptability both experimentally and theoretically. [more]